In a project with the investigators from the Section on Quantitative Imaging and Tissue Sciences (SQITS), NICHD and the Nervous System Development and Plasticity Section (NSDPS), NICHD we developed different models of myelin plasticity, or generally, delay plasticity in which we show how the stability of the synchronized state in the network, as well as general stability of the system, relies on having such adaptive delay. The consequences of such adaptive time delays are studied for three main cases: a) where the plasticity is activity-dependent; b) where the plasticity depends on the time arrivals of the postsynaptic and presynaptic action potentials; and c) where the plasticity depends on the oligodendrocytes myelinated multiple axons. For (a), we studied the effect of activity-dependent adaptive time delays on the stability of the system of coupled oscillators, with its implications on the stability of the oscillations and synchrony in the brain, while b) and c) are studied in terms of synchronization in the spiking neural networks. A manuscript describing this work is in preparation. In collaboration with NSDPS and SQITS, we studied the dynamic regulation of myelin by the surrounding glial cells and show that it is regulated by the astrocytes based on the level of activity present in an axon. We elucidated the biological mechanisms by which astrocytes regulate this process and use a theoretical framework to predict how the changes in myelin thickness, as well as the increase in the nodal width, affects the propagation of the signals along a myelinated axon, and to experimentally measure conduction speeds using a data analysis framework we implemented. The theoretical and experimental results were in a very good agreement. This work has been published the Proceedings of the National Academy of Sciences (PNAS) in November of 2018. In 2019 I joined the investigators in SQITS in their ongoing double pulsed-field gradient (dPFG) MRI study of tissue microstructure in traumatic brain injury. In a newly started project, we are applying the machine learning framework previously developed for the study of patterns of gene expression in developing zebrafish to help with identifying regions of traumatic brain injury using a number of different MRI stains. We continue research under the R24 BRAIN Initiative grant (In Vivo Brain Network Latency Mapping) in which several other principal investigators from NIH (NICHD, NINDS, and NIMH) are participated. The goal of this effort is to measure the temporal latencies (i.e., the time delays) in a human brain signaling between different regions. Being able to measure the latency information in vivo can provide a great diagnostic tool for a number of neuropsychiatric disorders. Our experimental protocol consists of four different imaging modalities: diffusion MRI, providing the structural information, electroencephalography (EEG), magnetoencephalography (MEG) and transcranial magnetic stimulation (TMS) which provides dynamic and functional information. In November of 2018 we presented our work at the Annual Meeting of the Society for Neuroscience in San Diego, CA. We showed the results obtained with the novel algorithms we developed for extracting latency information from the time series data. In 2019 these algorithms are being validated using electrocorticography (ECoG) data and will be used on a newly acquired MEG time-series data, addressing situations for which the latency information between pairs of cortical regions is well known.